The deep image prior approach has been introduced in inverse problems to exploit the structural bias of CNNs to natural image structure rather than noise [1]}. The regularized reconstruction of a static image from undersampled and noisy measurements are posed as
\(\lbrace \theta ^*\rbrace  = \arg \min _{\theta }\left\Vert \mathcal {A}(\mathbf {x}) - \mathbf {b}\right\Vert ^2 ~~\mbox{such that} ~~ \mathbf {x} = \mathcal {G}_{\theta }[\mathbf {z}]\) 
